Word representation
Unsupervised Cross Domain Word Representation Learning
... the word representation learning step, it is impor- tant for the domain adaptation tasks in which we subsequently evaluate the learnt word representa- ...
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Distributed Word Representation Learning for Cross Lingual Dependency Parsing
... independent word representations to ad- dress cross-lingual dependency parsing, which aims to predict the dependency parsing trees for sentences in the target language by training a dependency parser with labeled ...
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Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces
... rare word similarity datasets address the unreliability issue of Stanford RW but still do not represent a real-world rare word ...training word embeddings). We believe that rare word ...
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Hallym: Named Entity Recognition on Twitter with Word Representation
... construct word embeddings via canonical correlation analysis (CCA) on a considerable amount of tweet data and show the efficacy of word ...Besides word embedding, we use part- of-speech (POS) tags, ...
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Leveraging Web Semantic Knowledge in Word Representation Learning
... enhance word representation learning (WRL) and achieves promising performance on many NLP ...joint word representation learning model to capture semantics from both semantic knowledge and text ...
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Deep Generative Model for Joint Alignment and Word Representation
... a point estimate cannot. Brazinskas et al. (2017) recast the skip-gram model as a conditional varia- tional auto-encoder. They induce a Gaussian den- sity for each occurrence of a word in context, and for that ...
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Glove: Global Vectors for Word Representation
... A main problem with HAL and related meth- ods is that the most frequent words contribute a disproportionate amount to the similarity measure: the number of times two words co-occur with the or and, for example, will have ...
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Using Factored Word Representation in Neural Network Language Models
... as word factors, e.g. the lemma of word, POS tags, ...these word factors have shown to be very helpful to improve the translation qual- ...or word classes are used in the framework of modern ...
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Improved Word Representation Learning with Sememes
... learn word representations by maxi- mizing the predictive probabilities between words and their ...on word affinity matrix to learn word ...each word, regardless of the fact that many ...
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VecShare: A Framework for Sharing Word Representation Vectors
... of word em- beddings adds the embeddings to VecShare by up- loading the data to the share, following a simple standard format with n + 1 fields where the first field is a word, and the remaining fields give ...
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Exploiting Word Internal Structures for Generic Chinese Sentence Representation
... averaged word vectors and averaged character ...with word vectors, and FastSen- t model which utilizes a encoder-decoder model and encodes sentences as averaged word embed- ...sentence ...
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A Unified Model for Word Sense Representation and Disambiguation
... that word representation- s can capture semantic and syntactic information between words (Mikolov et ...better word representation- s (Bordes et ...for word sense representa- tion and ...
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HIT SCIR at MRP 2019: A Unified Pipeline for Meaning Representation Parsing via Efficient Training and Effective Encoding
... Our system architecture is shown in Figure 1. In this section, we will first introduce the transition- based parser in Section 2.1, which is the central part of our system. Then, to speed up the train- ing of stack LSTM ...
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Online Learning of Interpretable Word Embeddings
... for word representation, denoted as non-negative sparse embedding (NNSE) (Mur- phy et ...interpretable word embeddings by applying non-negative con- straints for word ...learns word ...
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Deep neural model with enhanced embeddings for pharmaceutical and chemical entities recognition in Spanish clinical text
... train word embeddings obtained from mul- tiple multilingual biomedical corpus to obtain multilingual word representations and test other word representation algorithms such as concept ...
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Can Network Embedding of Distributional Thesaurus Be Combined with Word Vectors for Better Representation?
... a word is a set of words having adequate con- text ...dense word vectors and investigate the usefulness of distributional thesaurus embedding in im- proving overall word ...proposed word ...
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Distributional Features for Text Categorization Based on Weight
... “bag-of-word” representation, previous researches usually assign a word with values that express whether this word appears in the document concerned or how frequently this word ...a ...
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Learning Orthographic Features in Bi directional LSTM for Biomedical Named Entity Recognition
... pre-trained word embeddings for representing words in a sentence; meanwhile, CNN-BiLSTM (Char-only) learns word representation from character embeddings using a convolutional neural ...
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Multi-Channel Distributed Representation for Classifying Tweets by using Convolutional Neural Networks
... Distributed representation indicates word vectors representing latent features of ...distributed representation enhances the representing ability of distributed ...distributed representation ...
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Semantically Enriched Weighted Word Embedding for Short Text Representation
... combines word embedding and Tf-idf information for semantic contents within very short text ...entire word vectors instead of separate ...combines word embedding with knowledge base but the ...
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